Assessing the value of hydroclimatic services for hydropower megadams: the case of the Grand Ethiopian Renaissance Dam

The recent advances in the skill of hydroclimatic services are motivating the need for quantifying their value in informing decisions. State-of-the-art forecasts proved to be skillful over seasonal and longer time scales especially in regions where climate teleconnections, such as El Nino Southern Oscillation, or particular hydrological characteristics, such as snow- and/or baseflow-dominance, enable predictability over such long lead times. Recent studies have investigated the value of seasonal streamflow forecasts in informing the operations of water systems in order to improve reservoir management strategies. However, how to best inform the operations of hydropower systems is still an open question because hydropower reservoir operations benefit from hydroclimatic services over a broad range of time scales, from short-term to seasonal and decadal time horizons, for combining daily and sub-daily operational decisions with strategic planning on the medium- to long- term.

In this work, we propose a machine-learning based framework to quantify the value of hydroclimatic services as their contribution to increasing the hydropower production of the Grand Ethiopian Renaissance Dam (GERD) in Ethiopia. The GERD, with an installed capacity of more than 6,000 MW is considered the largest hydroelectric power plant in Africa and the seventh largest in the world. Its construction is part of the strategic hydropower development plan in Ethiopia that aims to serve the growing domestic and foreign electricity demands. The quantification of the forecasts value relies on the Information Selection Assessment framework, which is applied to a service based on bias adjusted ECMWF SEAS5 seasonal forecasts used as input to the World-wide HYPE hydrological model. First, we evaluate the expected value of perfect information as the potential maximum improvement of a baseline operating policy relying on a basic information with respect to an ideal operating policy designed under the assumption of perfect knowledge of future conditions. Second, we select the most informative lead times of inflow forecast by employing input variable selection techniques, namely the Iterative Input Selection algorithm. Finally, we assess the expected value of sample Information as the performance improvement that could be achieved when the inflow forecast for the selected lead time is used to inform operational decisions. In addition, we analyze the potential value of forecast information under different future climate scenarios.

Preliminary results show that the maximum space for increasing the hydropower production of the GERD baseline operations not informed by any forecast is relatively small. This potential gain becomes larger when we focus on the performance during the heavy rainy season from June to September (Kiremt season), making room for the uptake of forecast information. The added production obtained with the forecast-informed operations of the GERD may represent an additional option in the current negotiations about the dam impacts on the downstream countries.

 

How to cite: Saoudi, Y., Crochemore, L., Pechlivanidis, I., and Giuliani, M.: Assessing the value of hydroclimatic services for hydropower megadams: the case of the Grand Ethiopian Renaissance Dam, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9720, https://doi.org/10.5194/egusphere-egu2020-9720, 2020